(A) MEG experimental paradigm. (B) Group-average frequency distribution of trials by SRT; white line = leftward saccades; blue line = rightward saccades. The white rectangle indicates the time interval selected for the single interval analysis in 4 out 6 subjects. (C) An example of the MEG source waveforms obtained by fMRI-constrained dipoles corresponding to the left IPS. Colored lines represent different subjects and the white line represents the grand average. (D) The fMRI group map, obtained by contrasting saccadic task periods with rest periods, is superimposed on the inflated cortex of one participant. White circles indicate the three ROIs for the left hemisphere.

Figure 1B illustrates the frequencydistribution of number of trials by SRT, averaged across subjects, respectively for leftward (white line) and rightward (blue line) saccades. Since SRT variability was high and MEG signals at the single trial level were noisy, a straight temporal average would not insure a goodestimate of the mean response. Two different approaches to improve temporal averaging were used. First, the shortest time interval (single SRT interval analysis) that contained more than 50% of all trials SRT was identified. This approachallowed us to average a large number of trials with relatively low SRT variability. For all subjects a 60 ms time interval met this criterion. In four subjects the interval was between 220 and 280 ms; in subject 5 the interval was between 200 and 260 ms, and in subjects 6 it was between 240 and 300 ms. Therefore, single subject mean estimates of the signal time course were obtained by averaging trials within this chosen 60 ms interval. Second, in order to study the variability of MEG signals as a function of SRT variability each participant's trials were dividedintoquartiles (SRT quartile analysis), and the signal time courses of all trials within each quartile were then averaged. To improve signal-to-noise, time courses for left and right saccades were averaged over each quartiles since no visual field difference was detected in the single interval analysis. However, we preserved the information concerning the direction of the saccade with respect to hemisphere, i.e., contralateral vs. ipsilateral to the hemisphere, to study the lateralization of these signals.

fMRI-guided source analysis of MEG data

The spatiotemporal distribution of the activity underlying the measured MEG signals was modeled in terms of multiple equivalent current dipoles (ECD) (Scherg, 1990). The electricalconductivity distribution of the head was assumedsphericallysymmetric. We performed an fMRI-constrained dipole analysis guided by the BOLD peaks of activation in the ROIs for both the single interval and the quartile analysis. This strategyprovided a basis for comparison and averaging of source waveforms across subjects. For each subject, an individual model was derived with six dipoles located at the ROIs identified by fMRI; the dipole orientation was allowed to change to fit measured MEG data. For each subject, measures of peak amplitude and latency of the source waveforms in each region were obtained in two steps. First, the peak of the averaged waveform was identified across subjects. Subsequently, we defined the individual peaks as the maximum amplitude of the waveform within a 100 ms interval centered on the peak of the averaged waveform. For the SRT single time interval analysis two within-3-way ANOVAs, with Hemisphere (left, right), Saccade Direction (contralateral, ipsilateral) and Region (FEF, IPS, MT+) as factors, were performed on the measures of peak latency and amplitude. For the SRT quartile analysis, we carried out two within-3-way ANOVAs with Quartile (Q1–Q4), Saccade Direction (contralateral, ipsilateral) and Region (FEF, IPS, MT+) as factors, on the measures of peak latency and amplitude.

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Results

Behavioral results

SRTs were measured during the MEG recordings. Subjects performed a total of 2931 trials (mean SRT = 265 ms) within the specified SRT limits (100–400 ms after visual cue). The percentage of accepted trials was on average 81% (range 71–89%), reflecting predominantly the exclusion of artifact epochs. At the group level, there was no significant SRT difference between leftward (1480 trials, mean RT = 266 ms) and rightward (1451 trials, mean RT = 263 ms) saccades. A single subject analysis didreveal a significant difference between left and right trials for subject 3 and 6 (p being faster than leftward saccades. A one-way ANOVAtesting for difference among subjects showed a significant effect (F = 97.97; p hoc contrasts (Bonferroni test) found that subjects 1 and 6 had significantly slower and subject 5 had significantly faster SRTs than the other subjects.

fMRI results

We performed a group analysis to estimate the averaged response across subjects. Figure 1D shows significant activations superimposed on the left hemisphere of one of the participants (fixed effect, p Consistent with previous studies of saccadic eye movements (Beauchamp et al., 2001; Berman et al., 1999; Corbetta et al., 1998) activation was observed at the intersection of Superior Frontal Sulcus and Precentral Sulcus, along the Precentral Sulcus, the Intraparietal Sulcus, the posteriorpart of the Middle Temporal Gyrus, and the visual cortex located on the Superior Occipital Gyrus. Additional activated regions were found in the Temporo-Parietal Junction and on the Inferior Parietal Lobule, bilaterally. On the medialsurface of the brain, activated regions were found along the primary and secondary visual areas and in the Supplementary Motor Cortex (corresponding to the Supplementary Eye Fields). The regions of activation on the Precentral Sulcus (PreCS) and the Intraparietal Sulcus (IPS) correspond, respectively, to the Frontal Eye Fields (FEF) and the Parietal Eye Field. The foci of activity on the middle temporal gyrus correspond to the human MT complex. A single subject analysis was also performed to localize these three functional regions for the MEG analysis. Robust activations were observed bilaterally for all subjects in the three ROIs (FEF, IPS, MT+); Table1provides their atlas coordinates (Talairach and Tournoux, 1988) and cluster size.

MEG results

Single SRT interval

The single interval analysis was run to determine the latency and amplitude of responses time-locked to the visual stimulus using a fairly homogenous set of trials with similar SRTs (seeMaterials and methodssection). An example of single subject and mean source waveforms from the fMRI guided source placed on the left IPS is shown in Figure 1C. Figure 2A shows the grandaverages of MEG signals aligned to the visual stimulus onset for contralateral and ipsilateral targets from the ROIs of the left and the right hemisphere.

Figure 2

Single SRT interval Analysis of MEG waveforms. (A) Forward grand averages of MEG source waveforms from the three ROIs (FEF, IPS, MT+). Red and blue lines represent respectively time courses for left and right hemisphere ROIs. Continuous and dashed lines indicate contralateral and ipsilateral target/saccade direction. The black vertical line indicates the visual stimulus onset. (B) ANOVA for single interval analysis: latency and main effect of ROIs (MT+, IPS, FEF). Vertical bars indicate standard errors of the mean (s.e.m). (C) Amplitude and main effect of Hemisphere (Contralateral, Ipsilateral).

SRT quartiles analysis

The single interval analysis showed an effect of response latency across regions. The next analysis considers how these regional differences in peak latency are related to SRT variability. Figure 3B shows the forward grand averages of MEG signals from the ROIs plotted in different colors for SRT quartiles. For displaycontra- and ipsilateral signals were collapsed. Since the previous analysis did not show any significant hemispheric difference, the source waveforms of left and right hemispheres were averaged, but information about saccade direction with respect to region was maintained.

Figure 3

SRT Quartiles Analysis of MEG waveforms. (A) Frequency distribution of trials by SRTs. The different colors show the division of trials by quartiles. (B) Forward grand averages of MEG source waveforms from the three regions of interest: FEF (top row), IPS (central row) and MT+ (bottom row) representing each quartile, collapsed across ipsi and contralateral regions. The black vertical line represents the visual stimulus onset.

With the exception of the first quartile, mean source waveforms for different quartiles appear to be shifted to the right following the order of quartiles; this effect is most evident in the MT+ source waveforms. Two random-effect within-3-way ANOVAs (Quartile × Saccade Direction × Region) were performed respectively on the individual measurements of peak latency and amplitude. The analysis of latency revealed a main effect of Quartile (F(3,15) = 15.65, p F(2,10) = 8.85; p interaction effect between these two factors. Also in this case, as for the single SRT interval analysis, planned comparison showed that MT+ peaked significantly earlier than FEF (F(1,5) = 19.1, p confirms the latency results obtained in the single interval analysis by using all available trials. IPS latency was not significantly different than MT+ or FEF. The effect of quartile indicates that latency increased as SRTs increased; planned comparisons revealed that the fourth quartile was different from the third (F(1,5) = 11.4, p F(1,5) = 52.4, p F(1,5) = 28.6, p F(1,5) = 6.6, p

Next, we calculated the nonparametric rankSpearmancorrelationcoefficient between the signal peak latency and SRT quartiles for each region, in order to identify which region showed the highest correlation between neural and behavioral latency. The repeated measurements used for the correlations were 6 subjects by 4 quartiles by 2 direction (contra-ipsi) resulting in a total of 48 observations for each region. MT+ had the highest correlation coefficient (R = 0.43; p followed by FEF (R = 0.38; p

Finally, the analysis of peak source amplitude showed only a significant effect of Saccade Direction (F(1,5) = 22.42; p confirming the greater activation of contralateral regions observed in the single SRT interval analysis.

Figure 4summarizes the results obtained in the quartiles. In all areas there is an increase in response latencies as function of SRT quartiles. However, there appears to be a large difference between mean behavioral variability and mean neural variability. For example, the difference in SRT between first and fourth quartile is about 130 ms, whereas the corresponding latency difference is ∼20 ms for FEF and IPS, and ∼30 ms for MT+. Therefore, a greatdeal of behavioral variability must be accounted for by later decision and motor processes.

Discussion

This combined fMRI-MEG study investigated the spatiotemporal dynamics of neural activation during visually guided saccades. The analysis focused on three regions that were robustly and bilaterally activated in our task as well as in previous neuroimaging studies (Astafiev et al., 2003; Beauchamp et al., 2001; Berman et al., 1999; Corbetta et al., 1998; Darby et al., 1996; Petit and Haxby, 1999): Frontal Eye Field (FEF) and Intraparietal Sulcus (IPS), both involved in sensorimotor transformation, and area MT+, predominantly sensory. The loci of these fMRI ROIs were used to seed the MEG data set and extract source time courses of neuromagnetic activity time-locked to the visual target. The wide spatial separation of these foci and their predominant activation across subjects (see Figure 1) makes this approach feasible. The most consistent visual evoked response peaked earlier in lateral occipital (MT+) than in parietal (IPS) and frontal (FEF) regions. We observed stronger neuromagnetic signals from the hemisphere contralateral to the saccade direction. Finally, there was a positive relationship between SRT variability and latency of visual responses both in sensory (MT+) and motor (FEF) regions.

Time course of visual activity

Although MEG recordings cannotdistinguish signals from different neuronalpopulations within the same area, as for example target selection or motor planning in IPS or FEF (Andersen et al., 1987; Barash et al., 1991; Bruce and Goldberg, 1985; Gnadt and Andersen, 1988; Goldberg et al., 2002; Mazzoni et al., 1996; Schall et al., 1995; Thompson et al., 1996, 1997), they allow the separation of an ‘averaged’ visual- or motor-related response from different regions by aligning trials to stimulus or movement onset, respectively. The alignment on the stimulus onset that was used in the present study was aimed at investigating the visual rather than the pre-motor activity, which would have been better detected with a backward averaging starting from movement onset. When a backward averaging was performed on the present dataset (data not shown), the signal-to-noise ratio was not as good as the one obtained with the forward procedure. Thus we decided to focus on visual processing, identified as the first peak of activity following stimulus onset.

In the single interval analysis the visual response in MT+ peaked at ∼157 ms followed by a response in IPS and FEF at ∼170 ms. Similar latencies were observed in the quartile analysis, where MT+, IPS and FEF regions showed a sequential activation following target onset. These latencies are consistent with previous saccadic EEG studies that also aligned trials to the visual target (Clementz et al., 2001; Evdokimidis et al., 1992; McDowell et al., 2005). For example, McDowell et al. (2005) observed greater stimulus-locked activity in the contralateral occipital cortex at about 130–170 ms after stimulus onset, during both pro- and anti-saccades. This sequence of activation over three cortical regions, which are anatomicallyconnected by both feedforward and feedback connnections (Felleman and VanEssen, 1991; Ungerleider and Desimone, 1986) shows a forward sweep of sensory activity that flows from posterior occipital to parietal and frontal areas for the first time in humans. Notwithstanding differences between MEG and single unit recordings, a similar trend was previously observed by Schmolesky and colleagues (1998), who reported increased latency of visual responses from MT (∼72 ms) to FEF neurons (∼80 ms). The timing of visual responses in awakebehavingmonkeys also shows this trend. The visual latency in FEF is around 70–80 ms (Schall, 1991; Thompson et al., 1996); in IPS it ranges from 40–50 ms (Bisley et al., 2004) to 110 ms (Barash et al., 1991); finally, in MT it is around 30–40 ms (Bair et al., 2002). The relativedelay in the measured MEG activity with respect to single unit also reflects differences in the way these two methodssample neural activity. While single unit studies measure when the signal departs from the baseline, in our study we focused on the latency of the peak of the neural activity, since the estimation of the peak of activity was considered more reliable when dealing with low signal-to-noise measurements.

Neural basis of saccadic reaction times variability

SRT variability was related to differences in neural latencies of the visual evoked activity across several cortical regions in occipital, parietal, and frontal cortex. The latency of visually evoked activity varied with SRT in frontal and occipital areas, i.e., slower latencies were associated with slower RTs. Prior monkey electrophysiological studies on FEF neurons emphasized the role of response preparation, showing that the time of saccade initiation did not vary as function of the timing of neuronal differences indexing target discrimination, but varied as function of the time FEF movement neurons exceeded a fixed constant threshold (Hanes and Schall, 1996; Thompson et al., 1996). However, more recent worksuggests that variability in sensory processing contributessubstantially to the latency of eye movement responses, especially if the visual task is designed to be demanding (Sato et al., 2001) or if monkeys locate the target by performing multiple eye movements (Ipata et al., 2006).

Carpenter (2004) observed that at least two factors contribute to the latency of response to a visual stimulus: lower level factors, such as the luminance and contrast of the visual stimulus, and higher level factors, such as the pressure to respond or stimulus probability. In this paradigm the onset of the target was indicated by a change in the color of the peripheral stimulus, a low level factor. Accordingly, the strongest relationship between neural activity and behavior was found in MT+ when time-locking trials on visual target onset. This also arguesagainst the possibility that this result was contaminated by motor preparation activity. If that were the case, we should have observed a stronger relationship in the fronto-parietal network rather than in a sensory area. However, the variability in visual responses explained only a small part of the behavioral variability, ∼20% based on the correlation analysis on SRT quartiles. Therefore, residual behavioral variability must be dependent on later stages of processing including decision, pre-motor and motor stages (Hanes and Schall, 1996; Shadlen and Newsome, 2001; Thompson et al., 1996).

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